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Azure ML and AWS SageMaker differ fundamentally in how they handle ML training jobs. Azure uses workspace-centric project management with role-based access control (RBAC) at the user level, while AWS employs job-level permissions through IAM roles. For data storage, Azure provides datastores and data assets within workspaces that abstract connection details, whereas AWS relies on S3 buckets with explicit URI paths and permission grants. Azure's approach suits teams managing user access centrally, while AWS fits organizations prioritizing granular job-level permissions and automation. The choice depends on existing cloud infrastructure, team structure, and MLOps workflow preferences.

Azure ML vs. AWS SageMaker: A Deep Dive into Model Training — Part 1